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Terentev A, Dolzhenko V. Can Metabolomic Approaches Become a Tool for Improving Early Plant Disease Detection and Diagnosis with Modern Remote Sensing Methods? A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:5366. [PMID: 37420533 DOI: 10.3390/s23125366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 05/25/2023] [Accepted: 06/04/2023] [Indexed: 07/09/2023]
Abstract
The various areas of ultra-sensitive remote sensing research equipment development have provided new ways for assessing crop states. However, even the most promising areas of research, such as hyperspectral remote sensing or Raman spectrometry, have not yet led to stable results. In this review, the main methods for early plant disease detection are discussed. The best proven existing techniques for data acquisition are described. It is discussed how they can be applied to new areas of knowledge. The role of metabolomic approaches in the application of modern methods for early plant disease detection and diagnosis is reviewed. A further direction for experimental methodological development is indicated. The ways to increase the efficiency of modern early plant disease detection remote sensing methods through metabolomic data usage are shown. This article provides an overview of modern sensors and technologies for assessing the biochemical state of crops as well as the ways to apply them in synergy with existing data acquisition and analysis technologies for early plant disease detection.
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Affiliation(s)
- Anton Terentev
- All-Russian Institute of Plant Protection, 196608 Saint Petersburg, Russia
| | - Viktor Dolzhenko
- All-Russian Institute of Plant Protection, 196608 Saint Petersburg, Russia
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2
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Farber C, Shires M, Ueckert J, Ong K, Kurouski D. Detection and differentiation of herbicide stresses in roses by Raman spectroscopy. FRONTIERS IN PLANT SCIENCE 2023; 14:1121012. [PMID: 37342141 PMCID: PMC10277736 DOI: 10.3389/fpls.2023.1121012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 05/18/2023] [Indexed: 06/22/2023]
Abstract
Herbicide application is a critical component of modern horticulture. Misuse of herbicides can result in damage to economically important plants. Currently, such damage can be detected only at symptomatic stages by subjective visual inspection of plants, which requires substantial biological expertise. In this study, we investigated the potential of Raman spectroscopy (RS), a modern analytical technique that allows sensing of plant health, for pre-symptomatic diagnostics of herbicide stresses. Using roses as a model plant system, we investigated the extent to which stresses caused by Roundup (Glyphosate) and Weed-B-Gon (2, 4-D, Dicamba and Mecoprop-p (WBG), two of the most commonly used herbicides world-wide, can be diagnosed at pre- and symptomatic stages. We found that spectroscopic analysis of rose leaves enables ~90% accurate detection of Roundup- and WBG-induced stresses one day after application of these herbicides on plants. Our results also show that the accuracy of diagnostics of both herbicides at seven days reaches 100%. Furthermore, we show that RS enables highly accurate differentiation between the stresses induced by Roundup- and WBG. We infer that this sensitivity and specificity arises from the differences in biochemical changes in plants that are induced by both herbicides. These findings suggest that RS can be used for a non-destructive surveillance of plant health to detect and identify herbicide-induced stresses in plants.
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Affiliation(s)
- Charles Farber
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
| | - Madalyn Shires
- Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, United States
| | - Jake Ueckert
- Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, United States
| | - Kevin Ong
- Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, United States
| | - Dmitry Kurouski
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
- Department of Molecular and Environmental Plant Science, Texas A&M University, College Station, TX, United States
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3
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Agustika DK, Mercuriani I, Purnomo CW, Hartono S, Triyana K, Iliescu DD, Leeson MS. Fourier transform infrared spectrum pre-processing technique selection for detecting PYLCV-infected chilli plants. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 278:121339. [PMID: 35537256 DOI: 10.1016/j.saa.2022.121339] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 04/12/2022] [Accepted: 04/29/2022] [Indexed: 06/14/2023]
Abstract
Pre-processing is a crucial step in analyzing spectra from Fourier transform infrared (FTIR) spectroscopy because it can reduce unwanted noise and enhance system performance. Here, we present the results of pre-processing technique optimization to facilitate the detection of pepper yellow leaf curl virus (PYLCV)-infected chilli plants using FTIR spectroscopy. Optimization of a range of pre-processing techniques was undertaken, namely baseline correction, normalization (standard normal variate, vector, and min-max), and de-noising (Savitzky-Golay (SG) smoothing, 1st and 2 derivatives). The pre-processing was applied to the mid-infrared spectral range (4000 - 400 cm-1) and the biofingerprint region (1800 - 900 cm-1) then the discrete wavelet transform (DWT) was used for dimension reduction. The pre-processed data were then used as an input for classification using a multilayer perceptron neural network, a support vector machine, and linear discriminant analysis. The pre-processing method with the highest classification model accuracy was selected for the further use in the processing. It was seen that only the SG 1st derivative method applied to both wavenumber ranges could produce 100% accuracy. This result was supported by principal component analysis clustering. Thus, we have demonstrated that by using the right pre-processing technique, classification success can be increased, and the process simplified by optimization and minimization of the technique used.
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Affiliation(s)
- Dyah K Agustika
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK; Department of Physics Education, Universitas Negeri Yogyakarta, Yogyakarta, 55281 Indonesia.
| | - Ixora Mercuriani
- Department of Biology Education, Universitas Negeri Yogyakarta, Yogyakarta, 55281 Indonesia
| | - Chandra W Purnomo
- Department of Chemical Engineering, Universitas Gadjah Mada, Sekip Utara Yogyakarta, 55281 Indonesia
| | - Sedyo Hartono
- Department of Plant Protection, Faculty of Agriculture, Universitas Gadjah Mada. Jl, Flora 1, Bulaksumur, Sleman 55281, Yogyakarta
| | - Kuwat Triyana
- Department of Physics, Universitas Gadjah Mada, Sekip Utara Yogyakarta, 55281 Indonesia
| | - Doina D Iliescu
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK
| | - Mark S Leeson
- School of Engineering, University of Warwick, Coventry CV4 7AL, UK.
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4
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Goff NK, Guenther JF, Roberts JK, Adler M, Molle MD, Mathews G, Kurouski D. Non-Invasive and Confirmatory Differentiation of Hermaphrodite from Both Male and Female Cannabis Plants Using a Hand-Held Raman Spectrometer. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27154978. [PMID: 35956927 PMCID: PMC9370318 DOI: 10.3390/molecules27154978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/15/2022] [Accepted: 08/03/2022] [Indexed: 11/19/2022]
Abstract
Cannabis (Cannabis sativa L.) is a dioecious plant that produces both male and female inflorescences. In nature, male and female plants can be found with nearly equal frequency, which determines species out-crossing. In cannabis farming, only female plants are preferred due to their high yield of cannabinoids. In addition to unfavorable male plants, commercial production of cannabis faces the appearance of hermaphroditic inflorescences, species displaying both pistillate flowers and anthers. Such plants can out-cross female plants, simultaneously producing undesired seeds. The problem of hermaphroditic cannabis triggered a search for analytical tools that can be used for their rapid detection and identification. In this study, we investigate the potential of Raman spectroscopy (RS), an emerging sensing technique that can be used to probe plant biochemistry. Our results show that the biochemistry of male, female and hermaphroditic cannabis plants is drastically different which allows for their confirmatory identification using a hand-held Raman spectrometer. Furthermore, the coupling of machine learning approaches enables the identification of hermaphrodites with 98.7% accuracy, whereas both male and female plants can be identified with 100% accuracy. Considering the label-free, non-invasive and non-destructive nature of RS, the developed optical sensing approach can transform cannabis farming in the U.S. and overseas.
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Affiliation(s)
- Nicolas K. Goff
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, USA
| | | | | | | | | | | | - Dmitry Kurouski
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, USA
- Correspondence: ; Tel.: +979-458-3778
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5
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Raman Method in Identification of Species and Varieties, Assessment of Plant Maturity and Crop Quality—A Review. Molecules 2022; 27:molecules27144454. [PMID: 35889327 PMCID: PMC9322835 DOI: 10.3390/molecules27144454] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/08/2022] [Accepted: 07/11/2022] [Indexed: 02/05/2023] Open
Abstract
The present review covers reports discussing potential applications of the specificity of Raman techniques in the advancement of digital farming, in line with an assumption of yield maximisation with minimum environmental impact of agriculture. Raman is an optical spectroscopy method which can be used to perform immediate, label-free detection and quantification of key compounds without destroying the sample. The authors particularly focused on the reports discussing the use of Raman spectroscopy in monitoring the physiological status of plants, assessing crop maturity and quality, plant pathology and ripening, and identifying plant species and their varieties. In recent years, research reports have presented evidence confirming the effectiveness of Raman spectroscopy in identifying biotic and abiotic stresses in plants as well as in phenotyping and digital selection of plants in farming. Raman techniques used in precision agriculture can significantly improve capacities for farming management, crop quality assessment, as well as biological and chemical contaminant detection, thereby contributing to food safety as well as the productivity and profitability of agriculture. This review aims to increase the awareness of the growing potential of Raman spectroscopy in agriculture among plant breeders, geneticists, farmers and engineers.
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Parlamas S, Goetze PK, Humpal D, Kurouski D, Jo YK. Raman Spectroscopy Enables Confirmatory Diagnostics of Fusarium Wilt in Asymptomatic Banana. FRONTIERS IN PLANT SCIENCE 2022; 13:922254. [PMID: 35837469 PMCID: PMC9275401 DOI: 10.3389/fpls.2022.922254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 05/27/2022] [Indexed: 06/15/2023]
Abstract
Fusarium oxysporum f. sp. cubense (FOC) causes Fusarium wilt, one of the most concerning diseases in banana (Musa spp.), compromising global banana production. There are limited curative management options after FOC infections, and early Fusarium wilt symptoms are similar with other abiotic stress factors such as drought. Therefore, finding a reliable and timely form of early detection and proper diagnostics is critical for disease management for FOC. In this study, Portable Raman spectroscopy (handheld Raman spectrometer equipped with 830 nm laser source) was applied for developing a confirmatory diagnostic tool for early infection of FOC on asymptomatic banana. Banana plantlets were inoculated with FOC; uninoculated plants exposed to a drier condition were also prepared compared to well-watered uninoculated control plants. Subsequent Raman readings from the plant leaves, without damaging or destroying them, were performed weekly. The conditions of biotic and abiotic stresses on banana were modeled to examine and identify specific Raman spectra suitable for diagnosing FOC infection. Our results showed that Raman spectroscopy could be used to make highly accurate diagnostics of FOC at the asymptomatic stage. Based on specific Raman spectra at vibrational bands 1,155, 1,184, and 1,525 cm-1, Raman spectroscopy demonstrated nearly 100% accuracy of FOC diagnosis at 40 days after inoculation, differentiating FOC-infected plants from uninoculated plants that were well-watered or exposed to water deficit condition. This study first reported that Raman spectroscopy can be used as a rapid and non-destructive tool for banana Fusarium wilt diagnostics.
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Affiliation(s)
- Stephen Parlamas
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
| | - Paul K. Goetze
- Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, United States
| | - Dillon Humpal
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
| | - Dmitry Kurouski
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
- Institute for Advancing Health Through Agriculture, Texas A&M University, College Station, TX, United States
| | - Young-Ki Jo
- Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, United States
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7
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Assess heavy metals-induced oxidative stress of microalgae by Electro-Raman combined technique. Anal Chim Acta 2022; 1208:339791. [PMID: 35525583 DOI: 10.1016/j.aca.2022.339791] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 03/25/2022] [Accepted: 03/29/2022] [Indexed: 01/16/2023]
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8
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Tanner F, Tonn S, de Wit J, Van den Ackerveken G, Berger B, Plett D. Sensor-based phenotyping of above-ground plant-pathogen interactions. PLANT METHODS 2022; 18:35. [PMID: 35313920 PMCID: PMC8935837 DOI: 10.1186/s13007-022-00853-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 02/08/2022] [Indexed: 05/20/2023]
Abstract
Plant pathogens cause yield losses in crops worldwide. Breeding for improved disease resistance and management by precision agriculture are two approaches to limit such yield losses. Both rely on detecting and quantifying signs and symptoms of plant disease. To achieve this, the field of plant phenotyping makes use of non-invasive sensor technology. Compared to invasive methods, this can offer improved throughput and allow for repeated measurements on living plants. Abiotic stress responses and yield components have been successfully measured with phenotyping technologies, whereas phenotyping methods for biotic stresses are less developed, despite the relevance of plant disease in crop production. The interactions between plants and pathogens can lead to a variety of signs (when the pathogen itself can be detected) and diverse symptoms (detectable responses of the plant). Here, we review the strengths and weaknesses of a broad range of sensor technologies that are being used for sensing of signs and symptoms on plant shoots, including monochrome, RGB, hyperspectral, fluorescence, chlorophyll fluorescence and thermal sensors, as well as Raman spectroscopy, X-ray computed tomography, and optical coherence tomography. We argue that choosing and combining appropriate sensors for each plant-pathosystem and measuring with sufficient spatial resolution can enable specific and accurate measurements of above-ground signs and symptoms of plant disease.
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Affiliation(s)
- Florian Tanner
- Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, SA Australia
| | - Sebastian Tonn
- Department of Biology, Plant-Microbe Interactions, Utrecht University, 3584CH Utrecht, The Netherlands
| | - Jos de Wit
- Department of Imaging Physics, Delft University of Technology, Lorentzweg 1, 2628 CJ Delft, The Netherlands
| | - Guido Van den Ackerveken
- Department of Biology, Plant-Microbe Interactions, Utrecht University, 3584CH Utrecht, The Netherlands
| | - Bettina Berger
- Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, SA Australia
| | - Darren Plett
- Australian Plant Phenomics Facility, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, SA Australia
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9
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Long Y, Huang W, Wang Q, Fan S, Tian X. Integration of textural and spectral features of Raman hyperspectral imaging for quantitative determination of a single maize kernel mildew coupled with chemometrics. Food Chem 2022; 372:131246. [PMID: 34818727 DOI: 10.1016/j.foodchem.2021.131246] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 08/26/2021] [Accepted: 09/26/2021] [Indexed: 01/14/2023]
Abstract
Maize mildew is a common phenomenon and it is essential to detect the mildew of a single maize kernel and prevent mildew from spreading around. In this study, a line-scanning Raman hyperspectral imaging system was applied to detect fungal spore quantity of a single maize kernel. Raman spectra were extracted while textural features were obtained to depict the maize mildew. Three kinds of modeling algorithms were used to establish the quantitative model to determine the fungal spore quantity of a single maize kernel. Then competitive adaptive reweighted sampling (CARS) was used to optimize characteristic variables. The optimal detection model was established with variables selected from the combination of Raman spectra and textural variance feature by PLSR. Results indicated that it was feasible to detect the fungal spore quantity of a single maize kernel by Raman hyperspectral technique. The study provided an in-situ and nondestructive alternative to detect fungal spore quantity.
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Affiliation(s)
- Yuan Long
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China
| | - Wenqian Huang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China.
| | - Qingyan Wang
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China
| | - Shuxiang Fan
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China
| | - Xi Tian
- Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China; Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100097, China; Beijing Key Laboratory of Intelligent Equipment Technology for Agriculture, Beijing 100097, China
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10
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Fang S, Zhao Y, Wang Y, Li J, Zhu F, Yu K. Surface-Enhanced Raman Scattering Spectroscopy Combined With Chemical Imaging Analysis for Detecting Apple Valsa Canker at an Early Stage. FRONTIERS IN PLANT SCIENCE 2022; 13:802761. [PMID: 35310652 PMCID: PMC8931522 DOI: 10.3389/fpls.2022.802761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 01/14/2022] [Indexed: 06/14/2023]
Abstract
Apple Valsa canker (AVC) with early incubation characteristics is a severe apple tree disease, resulting in significant orchards yield loss. Early detection of the infected trees is critical to prevent the disease from rapidly developing. Surface-enhanced Raman Scattering (SERS) spectroscopy with simplifies detection procedures and improves detection efficiency is a potential method for AVC detection. In this study, AVC early infected detection was proposed by combining SERS spectroscopy with the chemometrics methods and machine learning algorithms, and chemical distribution imaging was successfully applied to the analysis of disease dynamics. Results showed that the samples of healthy, early disease, and late disease sample datasets demonstrated significant clustering effects. The adaptive iterative reweighted penalized least squares (air-PLS) algorithm was used as the best baseline correction method to eliminate the interference of baseline shifts. The BP-ANN, ELM, Random Forest, and LS-SVM machine learning algorithms incorporating optimal spectral variables were utilized to establish discriminative models to detect of the AVC disease stage. The accuracy of these models was above 90%. SERS chemical imaging results showed that cellulose and lignin were significantly reduced at the phloem disease-health junction under AVC stress. These results suggested that SERS spectroscopy combined with chemical imaging analysis for early detection of the AVC disease was feasible and promising. This study provided a practical method for the rapidly diagnosing of apple orchard diseases.
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Affiliation(s)
- Shiyan Fang
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, China
- Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, China
| | - Yanru Zhao
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, China
- Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, China
| | - Yan Wang
- College of Plant Protection, Northwest A&F University, Yangling, China
| | - Junmeng Li
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, China
- Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, China
| | - Fengle Zhu
- School of Computer and Computing Science, Zhejiang University City College, Hangzhou, China
| | - Keqiang Yu
- College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, China
- Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, China
- Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, China
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11
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Payne WZ, Dou T, Cason JM, Simpson CE, McCutchen B, Burow MD, Kurouski D. A Proof-of-Principle Study of Non-invasive Identification of Peanut Genotypes and Nematode Resistance Using Raman Spectroscopy. FRONTIERS IN PLANT SCIENCE 2022; 12:664243. [PMID: 35058940 PMCID: PMC8765701 DOI: 10.3389/fpls.2021.664243] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 11/24/2021] [Indexed: 05/11/2023]
Abstract
Identification of peanut cultivars for distinct phenotypic or genotypic traits whether using visual characterization or laboratory analysis requires substantial expertise, time, and resources. A less subjective and more precise method is needed for identification of peanut germplasm throughout the value chain. In this proof-of-principle study, the accuracy of Raman spectroscopy (RS), a non-invasive, non-destructive technique, in peanut phenotyping and identification is explored. We show that RS can be used for highly accurate peanut phenotyping via surface scans of peanut leaves and the resulting chemometric analysis: On average 94% accuracy in identification of peanut cultivars and breeding lines was achieved. Our results also suggest that RS can be used for highly accurate determination of nematode resistance and susceptibility of those breeding lines and cultivars. Specifically, nematode-resistant peanut cultivars can be identified with 92% accuracy, whereas susceptible breeding lines were identified with 81% accuracy. Finally, RS revealed substantial differences in biochemical composition between resistant and susceptible peanut cultivars. We found that resistant cultivars exhibit substantially higher carotenoid content compared to the susceptible breeding lines. The results of this study show that RS can be used for quick, accurate, and non-invasive identification of genotype, nematode resistance, and nutrient content. Armed with this knowledge, the peanut industry can utilize Raman spectroscopy for expedited breeding to increase yields, nutrition, and maintaining purity levels of cultivars following release.
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Affiliation(s)
- William Z. Payne
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
| | - Tianyi Dou
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
| | - John M. Cason
- Texas A&M AgriLife Research, Stephenville, TX, United States
| | | | - Bill McCutchen
- Texas A&M AgriLife Research, Stephenville, TX, United States
| | - Mark D. Burow
- Department of Plant and Soil Science, Texas Tech University, Lubbock, TX, United States
- Texas A&M AgriLife Research, Lubbock, TX, United States
| | - Dmitry Kurouski
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
- The Institute for Quantum Science and Engineering, Texas A&M University, College Station, TX, United States
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12
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Chung PJ, Singh GP, Huang CH, Koyyappurath S, Seo JS, Mao HZ, Diloknawarit P, Ram RJ, Sarojam R, Chua NH. Rapid Detection and Quantification of Plant Innate Immunity Response Using Raman Spectroscopy. FRONTIERS IN PLANT SCIENCE 2021; 12:746586. [PMID: 34745179 PMCID: PMC8566886 DOI: 10.3389/fpls.2021.746586] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 09/17/2021] [Indexed: 06/13/2023]
Abstract
We have developed a rapid Raman spectroscopy-based method for the detection and quantification of early innate immunity responses in Arabidopsis and Choy Sum plants. Arabidopsis plants challenged with flg22 and elf18 elicitors could be differentiated from mock-treated plants by their Raman spectral fingerprints. From the difference Raman spectrum and the value of p at each Raman shift, we derived the Elicitor Response Index (ERI) as a quantitative measure of the response whereby a higher ERI value indicates a more significant elicitor-induced immune response. Among various Raman spectral bands contributing toward the ERI value, the most significant changes were observed in those associated with carotenoids and proteins. To validate these results, we investigated several characterized Arabidopsis pattern-triggered immunity (PTI) mutants. Compared to wild type (WT), positive regulatory mutants had ERI values close to zero, whereas negative regulatory mutants at early time points had higher ERI values. Similar to elicitor treatments, we derived an analogous Infection Response Index (IRI) as a quantitative measure to detect the early PTI response in Arabidopsis and Choy Sum plants infected with bacterial pathogens. The Raman spectral bands contributing toward a high IRI value were largely identical to the ERI Raman spectral bands. Raman spectroscopy is a convenient tool for rapid screening for Arabidopsis PTI mutants and may be suitable for the noninvasive and early diagnosis of pathogen-infected crop plants.
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Affiliation(s)
- Pil Joong Chung
- Temasek Life Science Laboratory, National University of Singapore, Singapore, Singapore
- Disruptive and Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
| | - Gajendra P. Singh
- Disruptive and Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
| | - Chung-Hao Huang
- Temasek Life Science Laboratory, National University of Singapore, Singapore, Singapore
- Disruptive and Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
| | - Sayuj Koyyappurath
- Temasek Life Science Laboratory, National University of Singapore, Singapore, Singapore
- Disruptive and Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
| | - Jun Sung Seo
- Temasek Life Science Laboratory, National University of Singapore, Singapore, Singapore
| | - Hui-Zhu Mao
- Temasek Life Science Laboratory, National University of Singapore, Singapore, Singapore
| | - Piyarut Diloknawarit
- Temasek Life Science Laboratory, National University of Singapore, Singapore, Singapore
| | - Rajeev J. Ram
- Disruptive and Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Rajani Sarojam
- Temasek Life Science Laboratory, National University of Singapore, Singapore, Singapore
- Disruptive and Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
| | - Nam-Hai Chua
- Temasek Life Science Laboratory, National University of Singapore, Singapore, Singapore
- Disruptive and Sustainable Technologies for Agricultural Precision, Singapore-MIT Alliance for Research and Technology, Singapore, Singapore
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Vallejo-Pérez MR, Sosa-Herrera JA, Navarro-Contreras HR, Álvarez-Preciado LG, Rodríguez-Vázquez ÁG, Lara-Ávila JP. Raman Spectroscopy and Machine-Learning for Early Detection of Bacterial Canker of Tomato: The Asymptomatic Disease Condition. PLANTS (BASEL, SWITZERLAND) 2021; 10:1542. [PMID: 34451590 PMCID: PMC8399098 DOI: 10.3390/plants10081542] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 07/17/2021] [Accepted: 07/22/2021] [Indexed: 12/20/2022]
Abstract
Bacterial canker of tomato is caused by Clavibacter michiganensis subsp. michiganensis (Cmm). The disease is highly destructive, because it produces latent asymptomatic infections that favor contagion rates. The present research aims consisted on the implementation of Raman spectroscopy (RS) and machine-learning spectral analysis as a method for the early disease detection. Raman spectra were obtained from infected asymptomatic tomato plants (BCTo) and healthy controls (HTo) with 785 nm excitation laser micro-Raman spectrometer. Spectral data were normalized and processed by principal component analysis (PCA), then the classifiers algorithms multilayer perceptron (PCA + MLP) and linear discriminant analysis (PCA + LDA) were implemented. Bacterial isolation and identification (16S rRNA gene sequencing) were realized of each plant studied. The Raman spectra obtained from tomato leaf samples of HTo and BCTo exhibited peaks associated to cellular components, and the most prominent vibrational bands were assigned to carbohydrates, carotenoids, chlorophyll, and phenolic compounds. Biochemical changes were also detectable in the Raman spectral patterns. Raman bands associated with triterpenoids and flavonoids compounds can be considered as indicators of Cmm infection during the asymptomatic stage. RS is an efficient, fast and reliable technology to differentiate the tomato health condition (BCTo or HTo). The analytical method showed high performance values of sensitivity, specificity and accuracy, among others.
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Affiliation(s)
- Moisés Roberto Vallejo-Pérez
- Consejo Nacional de Ciencia y Tecnología-Universidad Autónoma de San Luis Potosí, CIACYT, Alvaro Obregon 64, Col. Centro, San Luis Potosí 78000, Mexico
- Coordinación para la Innovación y la Aplicación de la Ciencia y la Tecnología (CIACYT), Universidad Autónoma de San Luis Potosí, Av. Sierra Leona 550, Col Lomas 2a. Sección, San Luis Potosí 78210, Mexico; (H.R.N.-C.); (L.G.Á.-P.); (Á.G.R.-V.)
| | - Jesús Antonio Sosa-Herrera
- Consejo Nacional de Ciencia y Tecnología-Centro de Investigación en Ciencias de Información Geoespacial A. C., Laboratorio Nacional de Geointeligencia, Aguascalientes 20313, Mexico;
| | - Hugo Ricardo Navarro-Contreras
- Coordinación para la Innovación y la Aplicación de la Ciencia y la Tecnología (CIACYT), Universidad Autónoma de San Luis Potosí, Av. Sierra Leona 550, Col Lomas 2a. Sección, San Luis Potosí 78210, Mexico; (H.R.N.-C.); (L.G.Á.-P.); (Á.G.R.-V.)
| | - Luz Gabriela Álvarez-Preciado
- Coordinación para la Innovación y la Aplicación de la Ciencia y la Tecnología (CIACYT), Universidad Autónoma de San Luis Potosí, Av. Sierra Leona 550, Col Lomas 2a. Sección, San Luis Potosí 78210, Mexico; (H.R.N.-C.); (L.G.Á.-P.); (Á.G.R.-V.)
| | - Ángel Gabriel Rodríguez-Vázquez
- Coordinación para la Innovación y la Aplicación de la Ciencia y la Tecnología (CIACYT), Universidad Autónoma de San Luis Potosí, Av. Sierra Leona 550, Col Lomas 2a. Sección, San Luis Potosí 78210, Mexico; (H.R.N.-C.); (L.G.Á.-P.); (Á.G.R.-V.)
| | - José Pablo Lara-Ávila
- Facultad de Agronomía y Veterinaria, Universidad Autónoma de San Luis Potosí, Km. 14.5 Carretera San Luis Potosí, Matehuala, Ejido Palma de la Cruz, Soledad de Graciano Sánchez, San Luis Potosí 78321, Mexico;
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Payne WZ, Kurouski D. Raman spectroscopy enables phenotyping and assessment of nutrition values of plants: a review. PLANT METHODS 2021; 17:78. [PMID: 34266461 PMCID: PMC8281483 DOI: 10.1186/s13007-021-00781-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 07/11/2021] [Indexed: 05/23/2023]
Abstract
Our civilization has to enhance food production to feed world's expected population of 9.7 billion by 2050. These food demands can be met by implementation of innovative technologies in agriculture. This transformative agricultural concept, also known as digital farming, aims to maximize the crop yield without an increase in the field footprint while simultaneously minimizing environmental impact of farming. There is a growing body of evidence that Raman spectroscopy, a non-invasive, non-destructive, and laser-based analytical approach, can be used to: (i) detect plant diseases, (ii) abiotic stresses, and (iii) enable label-free phenotyping and digital selection of plants in breeding programs. In this review, we critically discuss the most recent reports on the use of Raman spectroscopy for confirmatory identification of plant species and their varieties, as well as Raman-based analysis of the nutrition value of seeds. We show that high selectivity and specificity of Raman makes this technique ideal for optical surveillance of fields, which can be used to improve agriculture around the world. We also discuss potential advances in synergetic use of RS and already established imaging and molecular techniques. This combinatorial approach can be used to reduce associated time and cost, as well as enhance the accuracy of diagnostics of biotic and abiotic stresses.
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Affiliation(s)
- William Z Payne
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, 77843, USA
| | - Dmitry Kurouski
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, 77843, USA.
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, 77843, USA.
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Farber C, Islam ASMF, Septiningsih EM, Thomson MJ, Kurouski D. Non-Invasive Identification of Nutrient Components in Grain. Molecules 2021; 26:3124. [PMID: 34073711 PMCID: PMC8197263 DOI: 10.3390/molecules26113124] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 05/19/2021] [Accepted: 05/22/2021] [Indexed: 12/03/2022] Open
Abstract
Digital farming is a modern agricultural concept that aims to maximize the crop yield while simultaneously minimizing the environmental impact of farming. Successful implementation of digital farming requires development of sensors to detect and identify diseases and abiotic stresses in plants, as well as to probe the nutrient content of seeds and identify plant varieties. Experimental evidence of the suitability of Raman spectroscopy (RS) for confirmatory diagnostics of plant diseases was previously provided by our team and other research groups. In this study, we investigate the potential use of RS as a label-free, non-invasive and non-destructive analytical technique for the fast and accurate identification of nutrient components in the grains from 15 different rice genotypes. We demonstrate that spectroscopic analysis of intact rice seeds provides the accurate rice variety identification in ~86% of samples. These results suggest that RS can be used for fully automated, fast and accurate identification of seeds nutrient components.
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Affiliation(s)
- Charles Farber
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, USA;
| | - A. S. M. Faridul Islam
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA; (A.S.M.F.I.); (E.M.S.); (M.J.T.)
| | - Endang M. Septiningsih
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA; (A.S.M.F.I.); (E.M.S.); (M.J.T.)
| | - Michael J. Thomson
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA; (A.S.M.F.I.); (E.M.S.); (M.J.T.)
| | - Dmitry Kurouski
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, USA;
- The Institute for Quantum Science and Engineering, Texas A&M University, College Station, TX 77843, USA
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Payne WZ, Kurouski D. Raman-Based Diagnostics of Biotic and Abiotic Stresses in Plants. A Review. FRONTIERS IN PLANT SCIENCE 2021; 11:616672. [PMID: 33552109 PMCID: PMC7854695 DOI: 10.3389/fpls.2020.616672] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 12/17/2020] [Indexed: 05/11/2023]
Abstract
Digital farming is a novel agricultural philosophy that aims to maximize a crop yield with the minimal environmental impact. Digital farming requires the development of technologies that can work directly in the field providing information about a plant health. Raman spectroscopy (RS) is an emerging analytical technique that can be used for non-invasive, non-destructive, and confirmatory diagnostics of diseases, as well as the nutrient deficiencies in plants. RS is also capable of probing nutritional content of grains, as well as highly accurate identification plant species and their varieties. This allows for Raman-based phenotyping and digital selection of plants. These pieces of evidence suggest that RS can be used for chemical-free surveillance of plant health directly in the field. High selectivity and specificity of this technique show that RS may transform the agriculture in the US. This review critically discusses the most recent research articles that demonstrate the use of RS in diagnostics of abiotic and abiotic stresses in plants, as well as the identification of plant species and their nutritional analysis.
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Affiliation(s)
| | - Dmitry Kurouski
- Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX, United States
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